Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q30/00—Commerce
  • G06Q30/02—Marketing; Price estimation or determination; Fundraising
  • G06Q30/0241—Advertisements
  • G06Q30/0251—Targeted advertisements
  • G06Q30/0255—Targeted advertisements based on user history
  • G06Q30/0256—User search
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/90—Details of database functions independent of the retrieved data types
  • G06F16/95—Retrieval from the web
  • G06F16/951—Indexing; Web crawling techniques
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q30/00—Commerce
  • G06Q30/02—Marketing; Price estimation or determination; Fundraising

Definitions

• the Internet enables access to a wide variety of web documents, e.g., video and/or audio files, web pages for particular subjects, news articles, etc. Such access to these web documents has likewise enabled opportunities for targeted advertising.
• web documents of particular interest to a user can be identified by a search engine in response to a user query.
• the query can include one or more search terms, and the search engine can identify and, optionally, rank the web documents based on the search terms in the query and present the web documents to the user (e.g., according to the rank).
• This query can also be an indicator of the type of information of interest to the user.
• the targeted advertisements can include links to landing pages, and the selection of a link can cause the landing page to be displayed on a web browsing device.
• Advertisers typically attempt to anticipate the specific queries submitted by users that may be related to the advertiser's product or service offered.
• the keywords specified by advertisers can include keywords related to the product or service offered by the advertiser. These keywords can be broadly matched to the product or service offered by the advertiser, e.g., the keyword “flower” may broadly match to "florist” in a web document. Such broad matching can, however, produce less than desirable results (e.g., fewer conversions).
• an advertiser may not identify a particularly relevant keyword (referred to as a "missing keyword"). Thus, a query including a missing keyword may be deemed less relevant to the advertiser's content. Accordingly, specific queries for products may sometimes not result in the selection of advertisements linking to landing pages that are highly relevant to the query.
• one aspect of the subject matter described in this specification can be embodied in methods that include the actions of identifying a candidate query from queries stored in a query log; generating relevancy scores for a plurality of web documents, each relevancy score associated with a corresponding web document and being a measure of the relevance of the candidate query to the web document; selecting a web document having an associated relevancy score that exceeds a relevancy threshold; and associating the selected web document with the candidate query.
• Other embodiments of this aspect include corresponding systems, apparatus, and computer program products.
• Another aspect of the subject matter described in this specification can be embodied in methods that include the actions of defining query extraction criteria, the query extraction criteria configured to identify queries related to a subject relevance; identifying a candidate query from the queries stored in a query log according to the extraction criteria; generating relevancy scores for a first set of web documents, each relevancy score associated with a corresponding web document in the first set of web documents and being a measure of the relevance of the candidate query to the web document; selecting web documents having an associated relevancy score that exceeds a relevancy threshold; and generating a query-page candidate tuple from the selected web documents and the candidate query.
• Other embodiments of this aspect include corresponding systems, apparatus, and computer program products.
• Fig. 1 is a block diagram of an example online environment.
• Fig. 2 is a block diagram illustrating an example operational process.
• Fig. 3 is a block diagram showing an example extraction process.
• Fig. 4 is a block diagram of an example candidate query-page process.
• Fig. 5 is a block diagram of an example filtering process.
• Fig. 6a is a block diagram illustrating an example association of query-page tuples with advertisements.
• Fig. 6b is a block diagram illustrating an example association of query-page tuples with an existing advertisement.
• Fig. 6c is a block diagram illustrating another example association of query-page tuples with an advertisement.
• Fig. 6d is a block diagram illustrating another example association of a query-page tuple with an advertisement.
• Fig. 7 is a block diagram illustrating an example association of query-page tuples with a query category.
• Fig. 8 is a flow diagram of an example process for identifying query-page candidate tuples.
• Fig. 9 is a flow diagram of an example process for query extraction.
• Fig. 10 is a flow diagram of an example process for filtering query-page candidate tuples.
• Fig. 11 is a flow diagram of an example process for associating a query-page tuple with an advertisement group.
• Fig. 12 is a flow diagram of an example process for associating a query with a category.
• Fig. 13 is an example computer system.
• Fig. 1 is a block diagram of an example online environment 100.
• the online environment 100 can facilitate the identification and serving of web documents, e.g., web pages, advertisements, etc., to users.
• a computer network 110 such as a local area network (LAN), wide area network (WAN), the Internet, or a combination thereof, connects advertisers 102, a search engine 112, publishers 106, and user devices 108.
• Example user devices 108 include personal computers, mobile communication devices, television set-top boxes, etc.
• the online environment 100 may include many thousands of advertisers, publishers and user devices.
• a user device such as user device 108a, can submit a search query 109 to the search engine 112, and a search results page 111 can be provided to the user device 108a in response to the search query 109.
• the search results page 111 can include one or more links to web documents provided by the publishers 106.
• the search query 109 can include one or more search terms.
• a search term can be of the form of one or more keywords submitted as part of a search query through a search engine 112 that is used to retrieve responsive search results.
• a user of the user device 108a can search for an online store to purchase a star shaped cake pan.
• the search query 109 submitted can be for "star cake pan.”
• the search terms in this example can be "star,” "cake,” and "pan.”
• the publishers 106 can include general content servers that receive a request in the form of the search query 109 for content (e.g., web documents related to articles, discussion threads, music, video, graphics, other web document listings, information feeds, product reviews, etc.), and retrieve links to content on the search results page 111 in response to the search query 109.
• content servers related to news content providers, retailers, independent blogs, social network sites, products for sale, or any other entity that provides content over the network 110 can be a publisher.
• the search engine 112 can index the content provided by the publishers 106 and advertisers 102 (e.g., an index of cached web documents such as web index 120) for later search and retrieval of search results 118 that are relevant to the queries.
• An exemplary search engine 112 is described in S. Brin and L. Page, "The Anatomy of a Large-Scale Hypertextual Search Engine," Seventh
• Search results can be identified and ranked by various relevancy scores, e.g., information retrieval ("IR") scores based on text of cached and indexed web documents, feature vectors of identified documents, and other search processing techniques.
• IR scores can be computed from, for example, dot products of feature vectors corresponding to a query and a document, page rank scores, and/or combinations of IR scores and page rank scores, and so on.
• the search results 118 can include, for example, lists of web document titles, snippets of text extracted from those web documents and hypertext links to those web documents, and may be grouped into a predetermined number (e.g., ten) of search results. Search results 118 can also be ranked by the search engine 112, and presented as content on the search results page 111.
• the search terms in the search query 109 control the search results 118 that are provided by the search engine 112 through the search results page 111.
• the search engine 112 can retrieve and rank search results 118 based on the search terms submitted through a search query 109. For example, a search query for "star cake pan” can produce search results that are related to online retailers of cake pans, based on the search terms "star,” "cake,” and "pan.”
• the search results page 111 can include advertisements 116, or can include executable instructions, e.g., JavaScriptTM instructions, that can be executed at the user device 108a to request advertisements 116 over the network.
• the advertisements 116 can be in the form of graphical advertisements, such as banner advertisements, text only advertisements, image advertisements, audio advertisements, video advertisements, advertisements combining one of more of any of such components, etc., or any other type of electronic advertisement document.
• the advertisements 116 also include embedded information, such as a links to landing pages. Any web document can be a landing page; a landing page is any web document that is, or can be, linked to from another web document, advertisement, or search result.
• the landing page can be a web document that describes and/or offers for sale the advertiser's product or service.
• the landing page can, for example, also be a homepage for the advertiser, e.g., a company's home page.
• the advertisements 116 can be selected by the advertising management system 104 based on the keywords of the search query submitted to the search engine 112.
• the advertisers 116 are associated with keywords, and when particular keywords are identified in search queries, the advertisements 116 that are associated with those keywords can be selected for display on the search results page 111.
• the advertisements can also be selected from an auction.
• advertisers 102 can select, or bid, an amount the advertisers are willing to pay for each interaction with an advertisement, e.g., a cost-per-click amount an advertiser pays when, for example, a user clicks on an advertisement.
• the cost-per-click can include a maximum cost-per-click, e.g., the maximum amount the advertiser is willing to pay for each click of advertisement based on a keyword.
• the rank of an advertisement that is displayed can be determined by multiplying the maximum cost-per-click for the advertisement by a quality score of the advertisement, the latter of which can be determined, in part, by the advertisement's relevance to the keywords of the query.
• the advertisement can then be placed among other advertisements in order of increasing or decreasing rank.
• the advertisement management system 104 can store the advertisement information in the advertisement data 124.
• the advertisement management system 104 can also store information related to advertising campaigns in the campaign data 126.
• the campaign data 126 can, for example, specify advertising budgets for advertisements, associate keywords with advertisements and landing pages, and specify when, where and under what conditions particular advertisements may be served for presentation.
• the advertisers 102, publisher 106, user devices 108, and/or the search engine 112 can also provide usage information to the advertisement management system 104.
• This usage information can include measured or observed user behavior related to advertisements 116 that have been served, such as, for example, whether or not a conversion or a selection related to an advertisement 116 has occurred.
• the advertisement management system 104 performs financial transactions, such as crediting the publishers 106 and charging the advertisers 102 based on the usage information.
• Such usage information can also be processed to measure performance metrics, such as a click-through rate ("CTR”), conversion rate, etc.
• CTR click-through rate
• a click-through can occur, for example, when a user of a user device, selects or "clicks" on a link to a web document returned by the publisher or the advertising management system.
• the CTR is a performance metric that is obtained by, for example, dividing the number of users that clicked on the web document, e.g., a link to a landing page, an advertisement 116, or a search result 118, by the number of times the web document was delivered.
• a "conversion" occurs when a user consummates a transaction related to a previously served advertisement 116. What constitutes a conversion may vary from case to case and can be determined in a variety of ways. For example, a conversion may occur when a user clicks on an advertisement 116, is referred to the advertiser's landing page, and consummates a purchase there before leaving that landing page. Other actions that constitute a conversion can also be used.
• the keywords that the advertisers 102 associate with advertisements can be selected based on keywords that the users may use when searching for information related to the commercial offering being advertised.
• a commercial offering can be any opportunity on a landing page for a transaction, e.g., the sale of a product or service.
• the advertisers 102 are able to associate their advertisements 116 for the commercial offering with the keywords of the query.
• a retailer of cake pans can anticipate that a user searching for cake pans will likely include the search terms "cake" and "pan” in their search query.
• the retailer of cake pans can associate its advertisement with the keywords "cake" and "pan.” Searches that include the keywords cake and pan can result in the presentation of an advertisement provided by the retailer of cake pans.
• Fig. 2 is a block diagram 200 illustrating an example operational process.
• the three phases include an extraction phase, a candidate query-page phase, and a filtering phase. These phases are illustrative only and more or fewer phases can be used.
• the query-page identifier 114 can identify that the query log 128 includes a query for "train cake pan.”
• the query for "train cake pan” can be considered commercially relevant if it was submitted in excess of a minimum frequency of submission threshold, and if it was not submitted in excess of the maximum frequency of submission threshold.
• the candidates are filtered (e.g., to create query-page tuples).
• the query-page tuples represent the subset of the query-page candidate tuples that meet one or more filtering criteria.
• a filter 208 can remove query-page candidate tuples if the tuple is not relevant to the commercial offering, e.g., queries that result in the identification of pages from the entire web index 120 that do not have a discernable intent, as measured by one or more statistical processes, and/or query-page candidate tuples for which the intent measure diverges from the intent measure of the identified landing pages from the entire web index.
• a suggestion vector and/or a query intent vector can be used to determine whether the candidate tuple is relevant to the commercial offering.
• filtered selections can be associated with an advertisement.
• a query-page tuple can be associated with an advertisement by associating the query with the advertisement, and linking the advertisement to the landing page of the tuple.
• These associations can be stored in ad groups 212, which in some implementations are a collection of associations of keywords, advertisements and landing pages.
• the candidate query of "train cake pan” can be associated with an advertisement that links to the landing page that offers train cake pans for the Online Store A.
• Fig. 3 is a block diagram 300 showing an example extraction process (e.g., associated with query-page identifier 114).
• a query extractor 302 identifies from the query log 128 a set of candidate queries that meet one or more extraction criteria 308.
• the extraction criteria 308 can include criteria regarding the frequency of submission of the query, the timing of the query, the type of the query, and other criteria.
• the frequency specified by the submission criterion can be selected to identify queries that occur at least a minimum number of times and occur less than a maximum number of times.
• the lower threshold can be selected to protect user privacy and to identify queries that are likely to again be submitted in the future.
• a query that is submitted less than 50 times per year may not be commercially relevant; instead, it may be a focused query submitted by one user.
• the upper threshold can be selected to filter out queries that are submitted frequently, as these queries tend to be either generic queries (e.g., "credit cards") or queries that are of topical or pop-culture interests (e.g., a famous person's name).
• the query extractor 302 may also use timing criteria to analyze the timing of the query to determine if the query is commercially relevant.
• a query may not have the same level of commercial relevancy at different times. For example, a query for "pirate eye patch" may be commercially relevant during Halloween because people are more likely to search for costumes during Halloween. That same query may not be commercially relevant during non- Halloween time periods.
• the query extractor 302 may also use type criteria to analyze the type of the query to determine if the query is commercially relevant. In some implementations, a query is not commercially relevant if it is not directed towards a commercial offering.
• the extraction criteria 308 can be used to eliminate queries that are educational, news related, or otherwise not directed towards a commercial offering. For example, the extraction criteria 308 can identify educational websites, news sites, current events and query phrases (such as "how to " queries, "history of." queries, etc.) as types of queries that are not directed to commercial offerings.
• a query is not commercially relevant if there are already advertisements associated with the query.
• the query may also not be commercially relevant if the query has a low click-through rate; or is an expansion of a stem query that has already been selected according to the extraction criteria 308, and so on.
• the query extractor 302 may encounter a series of unrelated queries as shown in table 1 that are possible candidate queries.
• the extraction criteria 308 may specify a minimum number of submissions in a one- month period of 50, and a maximum number of submissions during that same one-month period of 50,000.
• the first query, "Mr. Celebrity,” is a very common query that was submitted more than the requisite minimum number of times. However, the frequency of submission on the first query also exceeded a maximum number of submissions. Thus, the query extractor 302 does not identify "Mr. Celebrity" as a candidate query.
• the other three queries, "red box,” “train cake pans,” and “Battle Tactics,” are queries that are submitted within the range of frequency of submission. Thus, each of these is identified as a candidate query.
• Fig. 4 is a block diagram 400 of an example candidate query-page process (e.g., associated with query-page identifier 114).
• a candidate query search evaluator 408 can use the search engine 112 and proper subset criteria 406 and the candidate queries to identify landing pages relevant to the candidate queries 306. The landing pages are used by the candidate query search evaluator 408 to identify query-page candidate tuples 410.
• the proper subset criteria 406 identifies web pages with commercial offerings by identifying the type of the web page. Pages such as news pages, blogs, forums, and the like are not included in the proper subset, while pages related to companies or retailer are included in the proper subset to be searched. These pages can be identified and differentiated by, for example, a list of domain names; top level domain extensions, such as .biz, .com, .org, .edu; or web sites.
• the proper subset criteria 406 identifies data that can be indicative of commercial offerings.
• the proper subset criteria 406 can include common phrases of commercial intent, e.g. "purchase,” "sale,” “shopping cart,” etc. Other criteria for determining whether a web page has a commercial offering can also be used, e.g., web pages that are linked to pages with commercial offerings can be considered as commercial offerings, and included in the proper subset. In some implementations, other considerations (i.e., other than commercial offering) can be used to evaluate the subset.
• the proper subset criteria 406 identifies web pages of advertisers 102 that have requested web pages to be searched. For example, an advertiser can provide a site map of its domain for inclusion in the proper subset criteria 406.
• the candidate query search evaluator 408 can cause the search engine 112 to search the proper subset of the web index 120 defined by the proper subset criteria 406 for landing pages related to the candidate queries 306.
• the search engine 112 can assign a relevancy score to each landing page returned from the proper subset of the web index 120 for each candidate query.
• the candidate queries 306 can include the query "train cake pans.”
• the search engine 112 can search the proper subset for landing pages responsive to the candidate query "train cake pans.” All landing pages responsive to "train cake pans" can be assigned relevancy scores indicated in Table 2.
• the candidate query search evaluator 408 can select landing pages identified from the proper subset based on the relevancy scores of the landing pages. In some implementations, the candidate query search evaluator 408 selects only the landing pages having a relevancy score that exceeds a relevance threshold. For example, if the relevance threshold is 85, then for the candidate query "train cake pans," the listed landing pages of Stores A, B, and C, each of which are assigned a relevancy score over the relevancy threshold of 85, are sufficiently relevant that they are selected by the candidate query search evaluator 408.
• These selected landing pages are then associated with the corresponding candidate query in a query-page candidate tuple 410.
• Table 3 lists the query-page candidate tuples for the candidate query "train cake pans.”
• the search engine 112 can be configured to perform a modified search on the proper subset of the web index 120 when identifying query-page candidate tuples. For example, estimated performance of the query, such as a predicted click through rate, can be omitted in a ranking process, and the ranking can be solely dependent on how relevant the candidate query is to the content of the web document. Other search algorithm modifications can also be made, e.g., ignoring keyword bids; ignoring geographic factors; and so on.
• Fig. 5 is a block diagram of an example filtering process (e.g., associated with query- page identifier 114).
• a filter 502 can be used to select from the query-page candidate tuples 410 the query-page tuples that meet one or more filtering criteria.
• the filtering criteria can, for example, include dominant intent measures, query-page intent measures, generic query lists, and/or other criteria that are selected to eliminate query- page candidate tuples that would not result in commercially viable advertising suggestions.
• the filter 502 can select from the candidate tuples 410 the tuples that likely present the better advertising opportunities to advertisers.
• a candidate tuple presents a likely advertising opportunity only where the dominant intent of the candidate query matches the intent of the selected landing pages of the candidate tuples 410. Thus, where there is no dominant intent of the candidate query, or where the dominant intent of the candidate query does not match the intent of the selected landing page, the candidate tuples 410 do not present a likely advertising opportunity.
• the dominant intent of the candidate query can be measured by use of an intent vector for the candidate query.
• the intent vector is a vector representation of the search results returned in response to using the candidate query to search the entire web index 120.
• the intent vector includes commonly associated terms from the identified landing pages, e.g., terms from the 10 highest ranked landing pages, for example.
• the filter 502 can use the terms in the intent vector to calculate an intent measure.
• the intent measure identifies whether the candidate query has a dominant intent.
• candidate queries for which the landing pages produce an intent vector with a high intent measure have a dominant intent; conversely, candidate queries for which the landing pages produce an intent vector with a low intent measure have no dominant intent.
• the low intent measure indicates that the candidate query may be a generic query, or may be a query that is a poor expression of the users' interests. For example, Table 4 identifies the terms commonly associated from landing pages identified by using the candidate query "train cake pan.”
• the dominant intent of the candidate query "Battle Tactics” can be determined by analysis of Table 7. All the terms commonly associated with the candidate query are related to the study of military tactics and warfare. Thus the candidate query "Battle Tactics" has a high intent measure related to the study of military tactics and warfare.
• the ad group classifier 602 identifies suggested advertisement groupings that pair query-page tuples 504 with advertisements.
• the pairings of advertisements with query-page tuples 504 can be suggested for association as suggestions 606, or the pairings can be automatically associated with each other into ad groups 212.
• the suggestions 606 can be presented to the advertisers through the advertiser front end 608.
• Fig. 6b is a block diagram 625 illustrating an example association of query-page tuples 504 with an existing advertisement in an ad group 212.
• the ad group 212 includes an advertisement that includes a link to a landing page.
• the landing page has also been identified in a query-page tuple by the query page identifier 114.
• Fig. 6d is another block diagram 675 illustrating an example association of a query- page tuple with an advertisement.
• the advertiser may not have an existing advertising campaign, and thus there is no existing ad data 212 with which the query-page tuple can be associated.
• Figs. 6b and 6c illustrate two example processes by which a query-page tuple that includes a selected candidate query and associated web document are associated with the ad group 212. Other association processes can also be used.
• the query-page tuple 504 can be used to suggest an advertisement for the advertiser 102.
• the advertiser 102 may receive a notification of an advertising opportunity for one of its landing pages and one or more suggested queries as defined by the query-page tuple 504. If the advertiser 102 accepts the suggestion, then corresponding advertising data 212 can be created for the advertiser.
• the advertiser 102 can provide a creative, bid information, and a budget to the advertising management system 104 to begin advertising offerings for the landing page indicated by the query-page tuple 504.
• the ad group classifier 602 can process a site map of the website of the advertiser 102 and can suggest advertising data for interior nodes of the site map for which the children landing pages are included in the query-page tuples 504.
• a retailer that sells clothing apparel may have a site map that includes a node "Shoes,” which, in turn, includes child nodes "Women's Shoes” and "Men's Shoes.”
• the retailer may offer, in corresponding web documents that are children of the "Women's Shoes” and "Men's Shoes” nodes, women's and men's shoes of a particular brand that are marketed by the shoe manufactures as being casual and comfortable shoes.
• the query-page identifier may identify query-page tuples 504 for each of these web documents and provide these suggestions to the retailer. Through use of the query-page tuples 504, the retailer can form an advertising campaign for the particular shoes.
• the query-page tuples 504 have other uses in addition to facilitating targeted advertising.
• the query-page tuples 504 can be used to generate a query- category map 704 that describes relevant user queries for certain categories.
• Fig. 7 is a block diagram 700 illustrating an example association of query-page tuples 504 with a query category.
• a query categorizer 702 can access the query-page tuples and a web directory 706 to generate the query-category map 704.
• the web director 706 can be a pre-existing directory of web documents classified according to hierarchal categories.
• Example web directories include the Open Directory Project, the Google Directory, or any other directory in which web documents are organized into categories.
• the query categorizer 702 can identify a category in the category directory to which the selected web document of the query-page tuple belongs, and can associate the candidate query with the identified category so that the candidate query can be presented in response to a selection of the identified category.
• query-page identifier 114 can use different extraction criteria, proper subset criteria, and filtering criteria for each category.
• the extraction criteria and the filtering criteria described above can be used when processing the web index 120 and query logs 128 for web properties that include commercial offerings.
• other extraction and filtering criteria can be used to identify relevant content for the other subject relevance.
• queries and/or pages that include the phrase "research paper" can be included when identifying query-page tuples 504 for an educational subject relevance, and queries and/or pages that include the term "shopping cart" can be excluded for educational subject relevance.
• the proper subset criteria can also be tailed to identify a subset of the web index 120 related to the subject relevance.
• the proper subset criteria can define a proper subset of web properties based on the web properties included in each category, and the subsequent processing to identify query-page tuples 504 can be limited to pages in each category subset.
• the query category 702 can identify a category to associate with the candidate query based on possible categorizations of the landing page.
• the query categorizer 702 can identify possible categorizations of the landing page based on the keywords in the landing page, for example.
• Facilitating targeted advertising and query categorization are two examples of how query-page tuples 504 can be used.
• query-page tuples 504 can be created for any type of relevance factor, e.g., commercial, educational, religious, political, etc., and can be used to facilitate more effective and efficient distribution of relevant information. For example, queries related to tax filings and that are relevant to a governmental agency's tax-related web documents can be identified and these web documents can be boosted in the search results page for those queries.
• relevance factor e.g., commercial, educational, religious, political, etc.
• Fig. 8 is a flow diagram of an example process 800 for identifying query-page candidate tuples.
• the process 800 can, for example, be implemented by the query-page identifier 114 of Fig. 1, and as described in Figs. 2-4.
• Fig. 9 is a flow diagram of process 900 for query extraction.
• the process 900 can, for example, be implemented by the query-page identifier 114 of figure 1, and/or the query extractor 302 of Fig. 3.
• the process 900 can, for example, be used to implement stage 802 of Fig. 8.
• Stage 902 identifies a query.
• the query-page identifier 114, or the query extractor 302 can identify a query from the query log 128.
• Fig. 10 is a flow diagram of an example process 1000 for filtering query-page candidate tuples.
• the process 1000 can, for example, be implemented by the query-page identifier 114 of Fig. 1 and/or the candidate query search evaluator 408 of Fig. 4 and the filter 502 of Fig. 5.
• the process 1000 can be used to filter the query-page candidate tuples generated by the process 800.
• Stage 1002 selects a candidate query-page tuple.
• the query-page identifier 114 of Fig. 1 and/or the candidate query search evaluator 408 can select a candidate query-page tuple from the query-page candidate tuples 410.
• Stage 1004 searches the collection of documents.
• the query-page identifier 114 or the candidate query search evaluator 408 can cause the search engine to search the entire web index 120 with the candidate query of the selected query-page candidate tuple.
• Stage 1006 generates a first vector.
• the query-page identifier 114 or the filter 502 can generate a suggestion vector for a web document identified in the query-page candidate tuple.
• the query-page identifier 114 or the filter 502 can determine the similarity measure between the suggestion vector and the intent vector.
• Stage 1012 determines if the similarity measure of the first vector to the second vector exceeds a threshold. For example, the query-page identifier 114 or the filter 502 determines if the similarity measure of the first vector to the second vector exceeds a threshold.
• Fig. 11 is a flow diagram of an example process 1100 for associating a query-page tuple with an advertisement group.
• the process 1100 can, for example, be implemented by the query-page identifier 114 or the ad group classifier 602 of Fig. 6.
• Stage 1102 compares the candidate query and the associated web document to an advertisement group.
• the query-page identifier 114 or the ad group classifier 602 can compare the keywords from the query-page tuples to keywords associated with advertisements in ad groups 212.
• the keywords of the query-page tuples can include keywords of the candidate queries, keywords of the associated web document, etc.
• the keywords of the advertisement group include keywords from search terms that the advertisement is associated with, keywords from the title of the advertisements and from landing pages associated with the advertisements, etc.
• Stage 1104 determines whether the candidate query and the associated web document are relevant to the advertisement group. For example, based on the comparison of stage 1102, the ad group classifier 602 can determine whether the query-page tuple is relevant to the advertisement group 212. For example, when the keywords associated with an advertisement group include one or more of the keywords of the candidate query, the ad group classifier 602 determines that the candidate query and the associated web document are relevant to the advertisement group.
• stage 1106 associates the candidate query and the web document with the advertisement group. For example, if the query-page identifier 114 or the ad group classifier 602 determines that the query-page tuple is relevant to the advertisement group, the candidate query can be associated with the advertisement group.
• the ad group classifier 602 can associate the candidate query with an existing advertisement, or it can generate a new advertisement based on the existing advertisements.
• Fig. 12 is a flow diagram of an example process 1200 for associating a query with a category.
• the process 1200 can, for example, be implemented by the query-page identifier 114 of Fig. 1 and/or the query categorizer 702 of Fig. 7.
• Stage 1202 identifies a query-page tuple.
• the query-page identifier 114 and/or the query categorizer 702 can identify a query-page tuple from the query page tuples 504.
• Stage 1204 identifies in a category directory the categories to which the associated landing page belongs.
• the query-page identifier 114 and/or the query categorizer 702 can identify a category in a web director to which the associated landing page of the selected query-page tuple belongs.
• Stage 1204 associates the candidate query with the identified category.
• the query-page identifier 114 and/or the query categorizer 702 can associate the candidate query of the selected query-page tuple with the category identified in stage 1204.
• Fig. 13 is block diagram of an example computer system 1300.
• the system 1300 can be used to implement the query page identifier 114 and/or the query extractor 302, candidate query search evaluator 408, filter 502, ad group classifier 602, and query categorizer 702 of Figs. 1-7. Other computer systems, however, can also be used.
• the system 1300 and includes a processor 1310, a memory 1320, a storage device 1330, and an input/output device 1340. Each of the components 1310, 1320, 1330, and 1340 can, for example, be interconnected using a system bus 1350.
• the processor 1310 is capable of processing instructions for execution within the system 1300. In one implementation, the processor 1310 is a single-threaded processor. In another implementation, the processor 1310 is a multi-threaded processor.
• the processor 1310 is capable of processing instructions stored in the memory 1320 or on the storage device 1330.
• the storage device 1330 is capable of providing mass storage for the system 1300.
• the storage device 1330 is a computer-readable medium.
• the storage device 1330 can, for example, include a hard disk device, an optical disk device, or some other large capacity storage device.
• the input/output device 1340 provides input/output operations for the system 1300.
• the input/output device 1340 can include one or more of a network interface devices, e.g., an Ethernet card, a serial communication device, e.g., and RS-232 port, and/or a wireless interface device, e.g., and 802.11 card.
• the input/output device can include driver devices configured to receive input data and send output data to other input/output devices, e.g., keyboard, printer and display devices 1360.
• Embodiments of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them.
• Embodiments of the subject matter described in this specification can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a tangible program carrier for execution by, or to control the operation of, data processing apparatus.
• the tangible program carrier can be a computer readable medium.
• the computer readable medium can be a machine readable storage device, a machine readable storage substrate, a memory device, a composition of matter effecting a machine readable propagated signal, or a combination of one or more of them.
• the processing devices disclosed herein encompass all apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers.
• the apparatus can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.
• a computer The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data.
• a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks.

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• 2009
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